Metadata for FAIR Data

Eva Méndez, Chair of the European Open Science Policy Platform, focused on the importance of metadata to make research data FAIR (Findable, Accessible, Interoperable, and Reusable). She stressed that research data always has to be described adequately in a way that is both human and machine readable. Open Science is a systemic change in the modus operandi of science and research, affecting the whole research cycle and its stakeholders.

FAIR data is not necessarily open: while open refers to the license, FAIR describes the quality of (meta-)data – and both aspects are equally important. This is reflected in the guidelines of many funding institutions (e.g. SNSF, Horizon 2020, EU guidelines) who state that data must be open by default and compatible with the FAIR principles, as this benefits both researchers and consumers. According to Méndez, FAIR data are approximately 80% metadata and 20% PIDs, depending on the discipline.

Furthermore, the speaker discussed what she calls «cool» metadata, namely data that does not change, gives as much information as possible, is FAIR, and is useful for others. Quality indicators of cool data are completeness, accuracy, conformance, consistency, coherence, timeliness, accessibility, openness (license), includes provenance, gives information about why data is not fully open (e.g. privacy issues), and explains how it can be accessed. She pointed out that each discipline moreover has different vocabularies for the description of metadata which can cause interoperability issues. Therefore, it can be difficult to translate good metadata from the lab to an open research infrastructure.

• Slides: Metadata →